Method for estimating a quantity of a blood component in a fluid receiver and corresponding error

ABSTRACT

A method and system for communicating estimated blood loss parameters of a patient to a user, the method comprising: receiving data representative of an image, of a fluid receiver; automatically detecting a region within the image associated with a volume of fluid received at the fluid receiver, the volume of fluid including a blood component; calculating an estimated amount of the blood component present in the volume of fluid based upon a color parameter represented in the region, and determining a bias error associated with the estimated amount of the blood component; updating an analysis of an aggregate amount of the blood component and an aggregate bias error associated with blood loss of the patient, based upon the estimated amount of the blood component and the bias error; and providing information from the analysis of the aggregate amount of the blood component and the aggregate bias error, to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.13/544,646 filed on 9 Jul. 2012, which claims the benefit of U.S.Provisional Patent Application Ser. No. 61/506,082, filed 9 Jul. 2011,U.S. Provisional Patent Application Ser. No. 61/646,818, filed 14 May2012, and U.S. Provisional Patent Application Ser. No. 61/646,822, filed14 May 2012, all of which are herein incorporated in their entireties bythis reference. This application also claims the benefit of U.S.Provisional Application Ser. No. 61/980,026, filed on 15 Apr. 2014,which is incorporated herein in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the surgical field, and morespecifically to a new and useful method for communicating estimatedblood loss parameters of a patient to a user for use in surgicalpractice.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A, 1B, and 1C are flowchart representations of differentembodiments of a method for communicating estimated blood lossparameters of a patient to a user;

FIG. 2A is a flowchart representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user;

FIG. 2B is a graphical representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user;

FIG. 3 is a graphical representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user;

FIGS. 4A-F are graphical representations of one variation of the methodfor communicating estimated blood loss parameters to a user;

FIG. 5 is a graphical representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user;

FIG. 6 is a graphical representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user;

FIG. 7 is a flowchart representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user;and

FIG. 8 is a graphical representation of one variation of the method forcommunicating estimated blood loss parameters of a patient to a user.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these preferred embodiments, butrather to enable any person skilled in the art to make and use thisinvention.

1. Method and Applications

As shown in FIGS. 1A, 1B, and 1C, a method 100 for communicatingestimated blood loss parameters of a patient to a user comprises:receiving data representative of an image of a fluid receiver S110;automatically detecting a region within the image associated with avolume of fluid received at the fluid receiver S120; calculating anestimated amount of the blood component present in the volume of fluid,and determining a bias error associated with the estimated amount of theblood component S130; updating an analysis of an aggregate amount of theblood component and an aggregate bias error associated with blood lossof the patient S140; and providing information derived from the analysisof the aggregate amount of the blood component and the aggregate biaserror, to the user S150.

As shown in FIGS. 2A and 2B, the method 100 can further include:calculating an estimated volume of blood in the fluid receiver based onat least one of the estimated amount of the blood component within thefluid receiver and a hematocrit of the patient, and determining a biaserror associated with the estimated volume of blood S132; calculating anaggregate blood loss of the patient based on the volume of blood in thefluid receiver and estimated volumes of blood collected in at least oneof the fluid receiver and other fluid receivers S142; updating ananalysis of an aggregate bias error in the aggregate blood loss of thepatient S152; and displaying the aggregate blood loss of the patient andthe aggregate bias error in the aggregate blood loss S162.

Generally, the method 100 functions to implement machine vision toestimate the amount of hemoglobin (e.g., intracellular hemoglobin, freehemoglobin, hemoglobin derivatives, etc.), whole blood, platelet plasma,white blood cells, or other blood component contained in one or morefluid receivers, such as a surgical textile (e.g., surgical gauze,sponge, a surgical dressing, a surgical towel, an absorbent pad, a teststrip, a drape, etc.) or a canister (e.g., suction canister, bloodsalvage canister, fluid receiving bag, a cell salvage system, a draindevice, etc.). In particular, the method 100 analyzes data derived froma (digital) photographic image of a fluid receiver (i.e., a “sample”) toidentify a region of the sample in the image, to estimate an amount(e.g., a mass, a weight) of a blood component in the sample based upon acolor parameter represented in the sample, to estimate and/or deriveerror-related metrics (e.g, bias, standard deviation, Bland-Altmanlimits-of-agreement, confidence intervals around these and othermetrics, etc.) in the estimated amount of the blood component in thesample, and to visually present the estimated amount of the bloodcomponent and the associated estimated bias error to a user (e.g., anurse, anesthesiologist, etc.) through a digital display or otherdigital user interface. The method 100 can similarly function toestimate amounts of non-blood components (e.g., saline, ascites, bile,irrigant saliva, gastric fluid, mucus, pleural fluid, fecal matter,urine, etc.) and associated errors (e.g., single-sample errors,aggregate errors, etc.), and to present this information to the user.The method 100 can also calculate aggregate amounts of blood componentsand/or non-blood components, determine corresponding aggregate biaserrors, and then provide information indicative of these aggregatevalues to the user, such as, for example, to assist the user in trackingpatient volemic status over time.

Therefore, rather than presenting a single value of estimated hemoglobinmass or aggregate blood volume in one or a series of fluid receivers,the method 100 can augment such values with corresponding estimatederror values, thereby enabling a user (e.g., a nurse, ananesthesiologist, a pediatrician, etc.) to better understand limitationsof the hemoglobin mass and blood volume estimates in real-time in aclinical setting. The method 100 can thus calculate aggregate errorvalues throughout a surgical operation or other clinical setting andpresent the additional error data to the user to enable the user toformulate a more complete clinical picture of the patient, therebyenabling the user to make a more informed decision of patient needs. Forexample, a user may handle or implement hemoglobin mass and aggregateblood volume estimates—output through the method—differently fordifferent types of patients (e.g., children, adults, and geriatrics,etc.) based on error estimates calculated and presented to the userthrough the method.

The method 100 can therefore implement/augment methods and techniquesdescribed in U.S. application Ser. No. 13/544,646 entitled “System andMethod for Estimating Extracorporeal Blood Volume in a Physical Sample”and filed on 9 Jul. 2012, U.S. application Ser. No. 13/894,054 entitled“System and Methods for Managing Blood Loss of a Patient” and filed on14 May 2013, U.S. application Ser. No. 13/738,919 entitled “System andMethod for Estimating a Quantity of a Blood Component in a FluidCanister” and filed on 10 Jan. 2013, and U.S. application Ser. No.14/072,625 entitled “Method for Triggering Blood Salvage” and filed on 5Nov. 2013 , which are each incorporated herein in its entirety by thisreference.

The method 100 can be useful in estimating an amount of a bloodcomponent such as whole blood, red blood cells, hemoglobin, platelets,plasma, white blood cells, or other blood component or combination ofblood components absorbed into a fluid receiver through non-contactmeans and in real-time, such as during a surgery or other medical event.A patient's blood loss and euvolemia status can thus be trackedaccording to the data, such as described in U.S. patent application Ser.No. 14/072,625. However, the method 100 can be applicable in any otherscenario or environment to estimate an amount of blood and/or a bloodcomponent in a fluid receiver. For example, the method can similarlyanalyze an image of an article of clothing, a ground, table, wall, orfloor surface, an external skin surface, a surgical glove, a surgicalimplement, or any other surface, material, substrate, or object toestimate an amount of blood component or non-blood component of apatient or other individual. Further, the method can estimate bloodcomponent metrics such as mass, volume, concentration, viscosity, and/orother suitable metric. Additionally or alternatively, the method 100 canbe useful in estimating an amount of a non-blood component such assaline, ascites, bile, irrigant saliva, gastric fluid, mucus, pleuralfluid, fecal matter, urine, or other bodily fluid through non-contactmeans and in real-time. Patient data relating to each tracked bodilyfluid can thus be charted and/or analyzed.

The method 100 can thus be implemented by a computing system thatfunctions as a fluid receiver analyzer in analyzing a photographic imageof a fluid receiver to estimate the quantity and/or quality of a fluid(or fluid component) contained therein. The computing system can includemodules in one or more of: a cloud-based system (e.g., Amazon EC2), amainframe computer system, a grid-computer system, and any othersuitable computer system. For example, the method can be implemented bya handheld (e.g., mobile) computing device, such as a smartphone, adigital music player, or a tablet computer executing a native bloodcomponent analysis application as shown in FIGS. 1A and 1B. For example,an image acquisition device integral within or detached from thecomputing device can capture an image of a fluid receiver, and aprocessor integrated into or detached from the computing device canimplement blocks of the method 100 to extrapolate from the image theamount of blood component and corresponding bias error with respect tothe fluid receiver. The computing system can additionally oralternatively communicate with a remote server, such as over theInternet via wireless or other radio-frequency connection, the servercan perform one or more Blocks of the method, and one or more outputs ofthe method can be transmitted from the remote server back to thecomputing device for further analysis and/or subsequent presentation toa user. Alternatively, one or more outputs of the method 100 can becalculated by the computing device and transmitted to the remote serverfor future reference. The computing device can also include or can becoupled to a digital display, and the method 100 can present informationto the user through the display.

Preferably, when performing one or more Blocks of the method 100, thecomputing device maintains connectivity with the remote server.Alternatively, the computing device can be disconnected from the remoteserver for some or all of the Blocks of the method. If the computingdevice loses connectivity with the server while performing the Blocks ofthe method, any network communications are preferably saved andresubmitted when connectivity is reestablished, enabling thecontinuation of the Blocks of the method 100.

The fluid receiver analyzer can further communicate (e.g., viaBluetooth) with another one or more systems implementing any of themethods described in U.S. application Ser. Nos. 13/544,646, 13/894,054,13/738,919, and/or 14/072,625 to form a fluid management system forgenerating a substantially comprehensive estimate of one or more bloodcomponents (e.g., extracorporeal blood volume, aggregate patient bloodloss), non-blood components, and/or biological statuses (e.g., patienteuvolemia status) based on patient fluids collected in fluid receivers.However, the method 100 can be implemented in or by any other computingsystem, computing device, or combination thereof.

Furthermore, variations of the method 100 can be adapted to process anyother set of measurements (e.g., discrete measurements taken at a set oftime points, measurements from video data, etc.), to determine an errorassociated with each of the set of measurements, to aggregate themeasurements and the errors associated with the measurements, and toprovide information derived from the aggregated measurements andaggregate errors to a suitable entity in order to enhance a subsequentanalysis or response to the set of measurements.

2. Estimated Amount of Blood Component

Block S110 recites: receiving data representative of an image of a fluidreceiver, Block S120 recites automatically detecting a region within theimage associated with a volume of fluid received at the fluid receiver,and Block S130 recites calculating an estimated amount of the bloodcomponent present in the volume of fluid based upon a color parameterrepresented in one or more regions, and determining an associated biaserror. Blocks S110, S120, and S130 can be implemented as described inU.S. application Ser. Nos. 13/544,646, 13/894,054, 13/738,919,14/072,625, and/or 14/687,842, which is incorporated herein in itsentirety by this reference.

In some variations, Block S140 can include, in part: estimating anaggregate amount of the blood component associated with blood loss ofthe patient. In one variation, Block S140 of the method can includeestimating an aggregate hemoglobin loss of a patient based upon the massof hemoglobin in the fluid receiver and estimated masses of hemoglobinaggregated from at least one of the fluid receiver and a set of otherfluid receivers. Thus, once Block S130 in this variation estimates anamount of hemoglobin in a single fluid receiver—shown in a currentphotographic image—Block S140 can sum this estimated amount with arunning tally of hemoglobin amounts derived from fluid received into thefluid receiver and/or fluid received into other fluid receivers capturedand analyzed in image data, thereby maintaining a total (i.e.,aggregate) estimate of the patient's hemoglobin loss (or a aggregateamount of patient hemoglobin collected in one or more fluid receivers).Block S140 can then pass this data to Block S150 for presentation to auser.

As shown in FIGS. 2A, 2B, and 6, another variation of the method 100 caninclude Block S132, which recites, in part: estimating a volume of bloodin the fluid receiver based on the mass of hemoglobin in the fluidreceiver and an estimated hematocrit of the patient. Preferably, thehematocrit is a known value that is manually entered by the user (e.g.,a nurse, an anesthesiologist, etc.). The hematocrit can be determinedthrough traditional calculations or approaches such as microhematocritcentrifugation. Alternatively, the hematocrit can be automaticallydetermined (e.g., algorithmically, automatically retrieved from anelectronic health record, etc.) and entered into the computing system byperforming Blocks of the method 100 on one or more volume of fluidsreceived by one or more fluid receivers.

In variations of the method 100 including Block S132, the method 100 canfurther include Block S142, which recites: estimating an aggregate bloodloss of the patient based on the volume of blood in the fluid receiverand estimated volumes of blood in the fluid receiver and/or other fluidreceivers, can similarly sum estimated blood volumes derived from thefluid receiver(s) to estimate an aggregate patient blood loss (or anaggregate amount of patient blood collected in one or more fluidreceivers) and then pass these data to Block S162 for presentation tothe user.

Alternatively, in one workflow including Blocks S120, S140, S142, andS152: Block S120 can implement machine vision techniques to identify aparticular type of the fluid receiver in the photographic image, andBlock S140 can group the current fluid receiver with fluid receivers ofthe same or similar type (e.g., in relation to manufacturer and model,material, and/or size, etc.). Block S140 can then sum estimatedhemoglobin quantities for all fluid receivers of the same or similartype, material, size, etc. and determine error estimation on a per-fluidreceiver-type basis for these aggregate hemoglobin quantity values.Block S142 can similarly sum estimated aggregate blood quantities forall fluid receivers of the same or similar type, material, size, etc.and pass these aggregate blood volume values to Block S152 for errorestimation on a per-fluid receiver-type basis.

Block S140 and Block S142 can also enable determination of the aggregatehemoglobin content and aggregate blood volume in the fluid receiver(s)for providing a (more) complete view of the patient's blood loss andeuvolemia status, as described in U.S. patent application Ser. No.14/072,625.

Generally, Blocks S110, S120, S130, S132, S140, and/or S142 alsoimplement methods or techniques described in U.S. patent applicationSer. Nos. 13/544,646, 14/072,625, and/or 13/738,919. However, BlocksS110, S120, S130, S132, S140, and/or S142 can implement any other methodor technique to estimate the amount of any blood component (e.g.,surgical gauze, sponge, a surgical dressing, a surgical towel, anabsorbent pad, a test strip, a drape, etc.) or non-blood component(e.g., saline, ascites, bile, irrigant saliva, gastric fluid, mucus,pleural fluid, urine, fecal matter) in a particular fluid receiver orother sample. Variations of Block S140, in relation to updating ananalysis of an aggregate bias error corresponding to an aggregateestimated amount of the blood component, are further described inSection 3 below:

3. Estimated Error

Block S140 recites, in part: updating an analysis of an aggregate biaserror corresponding to an aggregate estimated amount of the bloodcomponent, and Block S152 of the method recites updating an analysis ofan aggregate bias error in the aggregate blood loss (“cEBL”) of thepatient. In variations, Block S140 functions to estimate an aggregatebias error corresponding to the aggregate estimated hemoglobin mass inone or more fluid receivers (“cEHM”). In particular, Block S130estimates a bias error for the estimated hemoglobin mass associated witheach image of the fluid receiver(s) and Block S140 sums these biaserrors as each additional image of the fluid receiver(s) is imaged andanalyzed. As shown in FIGS. 2A and 2B, Block S152 similarly functions toestimate an aggregate bias error based on individual bias errors for theestimated amounts of blood loss with respect to the individual volumesof fluids associated with one or more fluid receivers.

In one implementation, Block S140 calculates a bias and a standarddeviation of aggregate error (“SD(error)”), which may indicate aprecision of the estimate of the aggregate bias error based on anout-of-sample study population. For example, Block S130 can estimatehemoglobin masses of 2 g, 6 g, 4 g, 1 g, 5 g, and 6 g, as well as biasesof 0.2 g, 1 g, 0.05 g, 0.5 g, 0.7 g, and 1 g for a sequence of sixsurgical gauze sponges. Block S140 can then estimate an aggregatehemoglobin mass of 24 g, an aggregate bias of +3.45 g, and a standarddeviation of aggregate bias of 2.5 g. Block S140 can thus output anaggregate estimated error of +0.95 g to +5.95 g, and, in this example,Block S150 can present to the user an aggregate estimated hemoglobinmass of 24 g +0.95 g to +5.95 g, thus indicating that the “true”aggregate hemoglobin volume in the six surgical gauze sponges fallsbetween 24.95 g and 29.95 g.

In another implementation, Block S140 calculates a bias and a standarddeviation of aggregate error (“SD(error)”), which may indicate aprecision of the estimate of the aggregate bias error based on anout-of-sample study population. For example, Block S130 can estimatehemoglobin masses of 2 g, 6 g, 4 g, 1 g, 5 g, and 6 g, as well as biasesof 0.2 g, 1 g, 0.05 g, 0.5 g, 0.7 g, and 1 g for a sequence of imagestaken of a canister as the canister receiving fluid from the patient.Block S140 can then estimate an aggregate hemoglobin mass of 24 g, anaggregate bias of +3.45 g, and a standard deviation of aggregate bias of2.5 g. Block S140 can thus output an aggregate estimated error of +0.95g to +5.95 g, and, in this example, Block S150 can present to the useran aggregate estimated hemoglobin mass of 24 g +0.95 g to +5.95 g, thusindicating that the “true” aggregate hemoglobin volume derived fromfluid filling the canister falls between 24.95 g and 29.95 g.

3.1 Per-Sample Bias and Standard Deviation Test Data

As shown in FIGS. 1A and 1B, variations of the method 100 fordetermining the bias error comprise generating a comparison between theestimated amount of a blood component and a set of ranges for amounts ofthe blood component, where the set of ranges for amounts of the bloodcomponent is determined from a set of fluid samples having a set of both(a) known amounts of the blood component paired with (b) estimatedamounts of the blood component derived from image analysis of each ofthe set of fluid samples within a version of the fluid receiver. A rangefrom the set of ranges is selected based upon the comparison, and thebias error and standard deviation in the bias error associated with therange is retrieved.

In one implementation, a per-sample bias (+/−SD) specific to a range ofpossible estimated amounts of blood component (e.g., estimated amountsof hemoglobin mass) is computed from (empirical) verification andvalidation data for each type of fluid receiver. For example, for eachfluid receiver type approved for imaging and analysis with the method, atest set of (at least) fifty samples associated with fluid receivers ofthe same size, material, manufacturer, and/or model can be tested tocalculate a per-sample bias specific to this type of fluid receiver(e.g., canister, surgical gauze sponge, absorbent pad, surgical textile,test strip, fluid receiving bag, drain system, cell salvage system,etc.). Alternatively, rather than associating per-sample biases (+/−SD)with ranges of values, the per-sample biases (+/−SD) can be specific todiscrete values of amounts of blood component. As such, directcomparison to the discrete values and/or interpolation between thediscrete values can be used in blocks of the method 100. Althoughspecific mappings of keys (e.g., ranges or discrete values of amounts ofblood component) to values (e.g., bias error (+/−SD)) have beendiscussed, any suitable type of key or value can be used for assigning abias error (+/−SD) to an estimated amount of blood or non-bloodcomponent.

In this implementation, the per-sample bias for a particular type offluid receiver can be calculated by creating a validation set ofsamples, each sample in the set containing a known (i.e., assayed)amount of the blood component (e.g., between 0 g and 6 g of hemoglobin).Each sample can then be imaged and analyzed—as in Blocks S110, S120, andS130—to generate corresponding image-based estimates of the amount ofthe blood component present in each sample. In one example, estimatedamounts of hemoglobin mass are calculated for each sample (e.g., sEHMvalues), the samples are then rank-ordered (i.e., sorted) by their sEHMvalues in ascending order, and this sorted dataset is then split intothe multiple sEHM subgroups, such as [sEHM≦1], [1<sEHM≦2], [2<sEHM≦3],[3<sEHM≦4], [4<sEHM≦5], and [sEHM>5]. A bias (+/−SD) of each subgroup iscomputed by comparing known hemoglobin mass against sEHM values for eachsample in a particular subgroup, and calculating an arithmetic mean ofthe differences (i.e., sEHM—known hemoglobin mass) for each samplewithin the particular subgroup. Finally, a standard deviation (“SD”) ofthe differences between biases of samples in a particular subgroup arecalculated (e.g., according to a standard “n-1” method for computingstandard deviation), and these standard deviations can be paired with acorresponding bias error for each subgroup in a lookup table (or othersuitable format) specific to the fluid receiver type, such as shown inFIGS. 1A and 1B. The lookup table can thus be stratified into discrete(e.g., one-gram (1.0 g)) intervals of predicted sEHM to provide anout-of-sample estimate of bias error (+/−SD) for fluid receivers withsEHM values falling within a particular interval. However, any othersample size, rank-ordering criteria, subgroup step size, standarddeviation method, etc. can be implemented to calculate a bias and/or astandard deviation for subgroups of samples where the amount of bloodcomponent is known and has been estimated through the Blocks of themethod 100.

In other variations, lookup tables can be generated for fluid receivertypes based on verification and validation data (or any other suitabletype of data), for any other suitable type of blood component (e.g.,whole blood, red blood cells, platelets, plasma, white blood cells,etc.) or non-blood component (e.g., saline, ascites, bile, irrigantsaliva, gastric fluid, mucus, pleural fluid, urine, fecal matter, etc.).Depending upon the type of blood component or non-blood componentanalyzed, the method 100 can include accessing the lookup tablecorresponding to the specific component type. For example, three sets oflookup tables can be stored in the same database, where each setcorresponds to one of three different types of blood components andwhere each set comprises lookup tables for approved (e.g., pre-analyzed)fluid receiver types. The method 100 can preferably access multiplelookup tables simultaneously to provide estimates of prediction bias(+/−SD) for different estimated amounts of blood components. In animplementation, three fluid receivers of different types (e.g., asponge, a suction canister, and a towel) are identified in one or moreimages. Estimated amounts of both platelet content and hemoglobin massare calculated for each of the volumes of fluid in the three fluidreceivers, resulting in six estimated amounts of blood components (i.e.,three platelet content amounts and three hemoglobin masses). The method100 can subsequently identify the six lookup tables that are specific tothe six possible combinations of fluid receiver type and blood componenttype (e.g., hemoglobin mass for sponge, platelet content for sponge,hemoglobin mass for suction canister, platelet content for suctioncanister, etc.). Estimates of prediction bias (+/−SD) can then be givenfor each of the six estimated amounts of blood component.

Bias and standard deviation values can thus be calculated for aparticular type of fluid receiver and/or blood component for storage ina lookup table (or other suitable format). Blocks S130, S132, S140, andS152 can recall one or more lookup tables specific to one or more typesof fluid receivers identified in a current image (e.g., identifiedthrough object recognition, object detection, etc.). In particular,Blocks S130, S132, S140, and S152 can apply data contained in theselected lookup table(s) to the estimated amount of blood component in acurrent sample to assign a bias and a standard deviation applicable tothe current sample.

Similar methods can be implemented to calculate bias (and +/−SD) inwhole blood volume estimates, such as through using a single validationset of samples assayed with blood of a static hematocrit value orthrough using a series of training sets of samples assayed with blood ofdifferent hematocrit values.

Such lookup tables can also be generated for different patientpopulation types, such as pediatric patients, adult patients, geriatricpatients, anemic patients, patients with histories of stroke, diabeticpatients, and any other suitable population type, and Blocks S130, S132,S140, and S152 can select particular error models or lookup tablesaccording to a characteristic or characterization of a current patient.Other patient characteristics can include, for example, medical history,genetics, gender, weight, age, height, race, health status, and/or diet.In an illustration, an amount of a blood component is estimated for afemale with a history of high blood pressure, and a lookup tablespecific to females with high blood pressure is used to provide the bias(+/−SD) for the estimated amount of blood component. In othervariations, lookup tables can be generated for different medicalprocedure characteristics (e.g., type of surgery, location of bloodloss, localization of blood loss, etc.). Generally, lookup tables can begenerated and tailored to any combination or number of fluid receivertypes, blood component types, patient characteristics, medical procedurecharacteristics, and/or other suitable types of information.

The lookup table can be stored, for example, in a database (e.g.,hierarchical, network, relational, object-oriented, etc.) accessiblethrough a database management system. The database can be located on thecomputer system, the remote server, or any other suitable platforms forholding databases. Alternatively, the lookup table can be storeddirectly in the memory of the computer system, the remote server, or anyother suitable platform with memory.

Alternatively, other approaches of providing a bias error for anestimated amount of blood or non-blood component can be used (e.g.,template matching, a parametric model, machine learning, implementationof bias errors associated with system elements used in the method 100,etc.). Such approaches can use information regarding any combination ornumber of fluid receiver types, blood component types, patientcharacteristics, medical procedure characteristics, and/or othersuitable types of information.

Additionally or alternatively, while bias error and standard deviation(e.g., in bias error) are determined in Blocks of the method 100described herein, variations of the method 100 can be adapted todetermine any other suitable statistics based upon a measurement and anaggregation of measurements including one or more of:limits-of-agreement (e.g., Bland-Altman limits-of-agreement), confidenceintervals, coefficients of determination (e.g., R²), root mean squareerrors, sum of square errors, sum of absolute errors, metrics derivedfrom an error histogram (e.g., an aggregation of parameters derived froman error histogram using a Monte Carlo simulation), an interquartilerange in an error metric, a minimum error value, a maximum error value,a mean error value, a median error value, and any other suitable type ofstatistic indicative of measurement/estimate quality. As such, in oneexample, an error histogram can be determined for each of a set ofmeasurements (e.g., derived from each of a set of sponges, derived fromeach of a set of measurements associated with a fluid receiver, etc.),and a Monte Carlo simulation can be used to compute a histogram of theaggregate error, based upon the error histograms from each of the set ofmeasurements. The method 100 can, however, implement any other suitablestatistical component indicative of error, in providing informationassociated with a medical parameter, to a user.

3.2 Estimated Error: Hemoglobin Mass—Surgical Textile

In one implementation, Block S140 calculates an aggregate cEHM bias(Δ_(T) ^(cEHM)) by summing estimated per-surgical textile biases of allsurgical textiles (e.g., surgical gauze, sponge, a surgical dressing, asurgical towel, an absorbent pad, a test strip, a drape, etc.) imagedand analyzed up to a current point as:

Δ_(T) ^(cEHM)=Σ_(i=1) ^(n)Δ_(i) ^(cEHM),

wherein Δ_(i) ^(cEHM) is the estimated cEHM bias of an individualsurgical textile i, such as specific to the surgical textile type and toa range of EHM values of the surgical textile i.

Block S150 can then calculate an aggregate cEHM standard deviation oferror (S_(T) ^(cEHM)) by summing estimated per-surgical textileSD(error) of all surgical textile images and analyzed up to the currentpoint as:

S _(T) ^(cEHM)=√{square root over (Σ_(i=1) ^(n)(S _(i) ^(cEHM))²)},

wherein S_(i) ^(cEHM) is the estimated cEHM standard deviation of errorof the surgical textile i, such as specific to the surgical textile typeand to a range of EHM values corresponding to the surgical textile i.

Block S130 can thus implement the lookup table described above to assignspecific cEHM bias (Δ_(i) ^(cEHM)) and cEHM standard deviation of error(S_(i) ^(cEHM)) values to each new surgical textile imaged and analyzedin Blocks S110 and S120, outputs of which can be used to update theanalysis in Block S140.

3.3 Estimated Error: Whole Blood Volume—Surgical Textile

Block S152 can similarly estimate an aggregate cEBL bias error (Δ_(T)^(cEBL)) of all surgical textiles (e.g., surgical gauze, sponge, asurgical dressing, a surgical towel, an absorbent pad, a test strip, adrape, etc.) imaged and analyzed up to a current point as:

${\Delta_{T}^{cEBL} = {\sum\limits_{i = 1}^{n}\; \frac{\Delta_{i}^{cEHM}}{{Hb}_{i}}}},$

wherein Δ_(i) ^(cEHM) is the cEHM bias of a surgical textile i, such asspecific to the surgical textile type and to a range of EHM values forthe surgical textile i, and wherein Hb_(i) is the user-entered estimateof the patient's laboratory derived hemoglobin (Hb) concentration (e.g.,in g/ml or g/dl) corresponding to the time at which surgical textile iwas used, as described in U.S. patent application Ser. No. 14/072,625.Additionally or alternatively, Hb_(i) can be determined in any othersuitable manner (e.g., using machine vision, using template matching,using a parametric model, etc.) as described in U.S. patent applicationSer. No. 14/072,625.

Block S152 can further calculate an estimated aggregate cEBL standarddeviation of error (S_(T) ^(cEBL)) as:

${S_{T}^{cEBL} = \sqrt{\sum\limits_{i = 1}^{n}\; \left( \frac{S_{i}^{cEHM}}{{Hb}_{i}} \right)^{2}}},$

wherein S_(i) ^(cEHM) is a standard deviation of a surgical textile i,such as specific to the type of surgical textile and the predicted cEHMof the surgical textile i. and wherein Hb_(i) is the user-enteredestimate of the patient's laboratory-derived Hb concentrationcorresponding to the time at which surgical textile i was used (orimaged). Additionally or alternatively, Hb_(i) can be determined in anyother suitable manner (e.g., using machine vision, using templatematching, using a parametric model, etc.) as described in U.S. patentapplication Ser. No. 14/072,625. In particular, because variances (s_(i)²) may be additive for independent samples, Block S140 can calculate theaggregate standard deviation (SD) of the error as a square root of theindividual sample variances.

Like Block S130 , Block S132 can thus implement a lookup table forestimated blood loss—as described above—to assign specific cEBL bias(Δ_(i) ^(cEBL)) and specific cEBL standard deviation of error (S_(i)^(cEBL)) values to each new surgical textile imaged and analyzed inBlocks S110, S120, and S132.

3.4 Estimated Error: Hemoglobin Mass—Canister

In another implementation, Block S140 calculates a Δ_(T) ^(cEHM) bysumming estimated per-canister biases of all canisters and/or fluidreceivers similar to canisters (e.g., suction canister, blood salvagecanister, fluid receiving bag, a cell salvage system, a drain device,etc.) imaged and analyzed up to a current point as:

Δ_(T) ^(cEHM)=Σ_(i=1) ^(n)Δ_(i) ^(cEHM),

wherein Δ_(i) ^(cEHM) is the estimated cEHM bias of a canister i, suchas specific to the canister type and to a range of EHM values of thecanister i.

Block S140 can then calculate a S_(T) ^(cEHM) by summing estimatedper-canister SD(error) of all canister images and analyzed up to thecurrent point as:

S _(T) ^(cEHM)=√{square root over (Σ_(i=1) ^(n)(S _(i) ^(cEHM))²)},

wherein S_(i) ^(cEHM)is the estimated cEHM standard deviation of errorof the canister i, such as specific to the canister type and a range ofEHM values corresponding to the canister i.

As with the surgical textiles, Block S130 can thus implement the lookuptable described above to assign Δ_(i) ^(cEHM) and S_(i) ^(cEHM) valuesto each new canister imaged and analyzed in Blocks S110, S120, and S130.

3.5 Estimated Error: Whole Blood Volume—Canister

Block S152 can similarly estimate a Δ_(T) ^(cEBL) of all canisters(e.g., suction canister, blood salvage canister, fluid receiving bag, acell salvage system, a drain device, etc.) imaged and analyzed up to acurrent point as:

${\Delta_{T}^{cEBL} = {\sum\limits_{i = 1}^{n}\; \frac{\Delta_{i}^{cEHM}}{{Hb}_{i}}}},$

wherein Δ_(i) ^(cEHM) is the cEHM bias of a canister i, such as specificto the canister type and to a range of EHM values of the canister i, andwherein Hb_(i) is the user-entered estimate of the patient's laboratoryderived Hb concentration (e.g., in g/ml or g/dl) corresponding to thetime at which canister i was used, as described in U.S. patentapplication Ser. No. 14/072,625. Additionally or alternatively, Hb_(i)can be determined in any other suitable manner (e.g., using machinevision, using template matching, using a parametric model, etc.) asdescribed in U.S. patent application Ser. No. 14/072,625.

Block S152 can further calculate an S_(T) ^(cEBL) as:

${S_{T}^{cEBL} = \sqrt{\sum\limits_{i = 1}^{n}\; \left( \frac{S_{i}^{cEHM}}{{Hb}_{i}} \right)^{2}}},$

wherein S_(i) is a standard deviation of a canister i, such as specificto the type of fluid receiver and the predicted cEHM of the canister i.and wherein Hb_(i) is the user-entered estimate of the patient'slaboratory-derived Hb concentration corresponding to the time at whichcanister i was used (or imaged). Additionally or alternatively, Hb_(i)can be determined in any other suitable manner (e.g., using machinevision, using template matching, using a parametric model, etc.) asdescribed in U.S. patent application Ser. No. 14/072,625. In particular,because variances, s_(i) ², may be additive for independent samples,Block S152 can calculate the aggregate SD of the error as a square rootof the individual sample variances.

Like Block S130 , Block S132 can thus implement a lookup table forestimated blood loss—as described above—to assign Δ_(i) ^(cEBL) andS_(i) ^(cEBL) values to each new canister imaged and analyzed in BlocksS110, S120, and S132.

3.6 Estimated Error: Cross Validation

In one variation, a user supplies ground truth values of EHM and EBL foran initial set of fluid receivers (e.g., during a surgery), such as bysoaking a sample in saline, wringing fluid output of the sample,centrifuging the bloodied saline, measuring a volume of red blood cellsinto the centrifuged fluid, and calculating a hemoglobin mass from thevolume of red blood cells. In this variation, Blocks S140 and S152 canfurther implement cross-validation (or other out-of-sample estimate) oferror in the cEHM and cEBL values, respectively, for each subsequentfluid receiver based on the ground truth values supplied by the user.For example, Block S140 can implement K-fold cross-validation or 2-foldcross-validation to assess applicability of the lookup table for thecorresponding fluid receiver type to the current single fluid receiveror set of fluid receivers, and Block S150 can further present this valueto the user.

However, Blocks S140 and S152 can calculate out-of-sample error from anin-vitro (i.e., controlled) setting and/or from clinical study in anyother way, and Blocks S140 and S152 can pass these data to Blocks S150and S162, respectively, for presentation to a user, such as when theuser selects or toggles the option at a user interface.

3.7 Estimated Error: Root Mean Square Error

In another variation, Block S140 maintains a running tally of the rootmean square error (“A_(RMS),” or root mean square deviation) of theaggregate estimated amount of blood component as Blocks S130 and S140estimate and sum an amount of blood component in each additional fluidreceiver. In this variation, Block S140 can thus generate an A_(RMS)that aggregates the magnitude of the bias error (mean differences) andassociated standard deviation (differences) values into a single measureof predictive power in units identical to the output variable forestimated amount of blood component (e.g., grams).

Block S140 can calculate an A_(RMS) value at time ‘t’ from an aggregatebias (“e_(T)”) and a aggregate variance (σ_(T) ²”) as:

A _(RMS)(t)=sqrt(σ_(T) ² +e _(T) ²).

Block S140 can further calculate a σ_(T) ² value and a e_(T) value ofcEHM at time t for each subsequently-imaged fluid receiver byimplementing a per-sample variance (“σ_(sample) ²”) and a per-samplebias (“e_(sample)”) (such as characterized in out-of-sample in-vitrotesting or clinical testing) according to:

e _(T)(t)=N(t)×e _(sample) and

σ_(T) ²(t)=N(t)*σ_(sample) ²,

wherein N(t) represents a number of fluid receivers scanned at time t(or within a limited time window, such as a two-minute time window)during a current surgical procedure.

Block S152 can similarly maintain a running tally of a root mean squareerror of the aggregate estimated blood loss as Blocks S132 and S142estimates and sums a whole blood volume in each additional fluidreceiver. Block S152 can thus similarly generate a root mean squarederror in units identical to the output variable for estimated blood loss(e.g., milliliters).

4. Providing Data

As shown in FIGS. 1A, 1B, 1C, and 3, Block S150 of the method recitesproviding information derived from the aggregate amount of the bloodcomponent and the aggregate bias error, from the analysis, to the user.As shown in FIG. 3, in variations, Block S150 of the method recitesdisplaying the aggregate hemoglobin loss of the patient and theaggregate error in the aggregate hemoglobin loss. As shown in FIG. 2Aand 2B, Block S162 similarly recites displaying the aggregate blood lossof the patient and the aggregate error in the aggregate blood loss.Generally, Blocks S150 and S162 can thus function to display estimatedbiases and/or standard deviations of bias for the values of cEHM andcEBL calculated in Blocks S140 and S152, respectively, thereby providinga user with a more comprehensive view of the accuracy (i.e., bias error)of cEHM and cEBL values calculated from images of fluid receivers.Alternatively, as shown in FIGS. 4A-F, 6, and 8, the user interface candisplay an estimated amount of a blood or non-blood component and itscorresponding bias error—along with the standard deviation of the biaserror—as outputted by Block S130 for a single volume of fluid receivedby a single fluid receiver. For example, Block S162 can display anestimated whole blood volume and its corresponding bias error—along withthe standard deviation of the bias error—for a single fluid receiver asoutput in Block S132. The user interface can also display an aggregateestimated whole blood volume for a set of fluid receivers as output inBlock S142.

Blocks S150 and S162 can thus interface with a digital display arrangedwithin an operating room or other surgical setting to render estimatedamounts of blood components, associated biases, and standard deviationsof biases for a single fluid receiver or an aggregate set of fluidreceivers. For example, Blocks S150 and S162 can interface with adigital display of a mobile computing device (e.g., a tablet, asmartphone, etc.) to visually display values calculated in Blocks S130,S132, S140, S142, and/or S152 to a user (e.g., a nurse, ananesthesiologist, etc.). However, Blocks S150 and S162 can present anyof these data to a user through any other suitable visual and/orauditory device.

The digital display can further include a user interface, and BlocksS150 and S162 can respond to inputs or mode selections made by a userthrough the user interface by adjusting a type, a combination, and/or aposition of one or more of hemoglobin mass, blood volume, or other bloodcomponent along with the calculated bias errors for a current case.Alternatively or in conjunction, the user can adjust a type,combination, and/or a position of one or more types or samples ofnon-blood components. As shown in FIG. 4F, the user interface can alsoallow the user to delete individual samples in the calculation of theaggregate estimate amount of a blood or non-blood component as well asits corresponding aggregate bias error. In one example implementation,Blocks S150 and S162 can toggle bias error and standard deviation valuesof aggregate estimated hemoglobin mass and estimated patient blood loss,respectively, in response to an input into the user interface. Inanother example implementation, Blocks S150 and S162 toggle through (orbetween) various sources of error calculated in Blocks S140 and S152,respectively, in response to inputs entered into the user interface by auser, such as according to an error type particularly applicable to acurrent patient or patient status, a current surgery type, etc. Forexample, during a surgery on an anemic patient, the user may select anerror type that was previously characterized in a study of methodaccuracy in an anemic patient sample population but later switch to anerror type that was characterized in a study of method accuracy in ageriatric patient sample population for a later surgery on a geriatricpatient. However, the user may select an error type that corresponds toany combination or number of fluid receiver types, blood componenttypes, patient characteristics, medical procedure characteristics,and/or other suitable types of information. In yet another exampleimplementation, Blocks S150 and/or S162 can display bias error in theestimated amount of blood component across the set of fluid receiverswith values for +/−1SD, +/−2SD, +/−3SD, or bias at 95% confidenceintervals, etc. based on an error type selected by the user. Forexample, the aggregate bias error displayed for cEHM can be displayed inthe form of calculated Bland-Altman Limits of Agreement as:

LOA^(cEHM)=total cEHM−Δ_(T) ^(cEHM)±1.96*S _(T) ^(cEHM).

In another example, the aggregate bias error displayed for cEBL can bedisplayed in Bland-Altman Limits of Agreement form as:

LOA^(cEBL)=total cEBL−Δ_(T) ^(cEBL)±1.96*S _(T) ^(cEBL).

In another implementation, Blocks S140 and/or S152 further calculateprecision-related error bars for the aggregate amount of blood componentand the aggregate estimated blood loss values, respectively. Blocks S150and/or S162 can display these precision-related error bars inconjunction with the corresponding accuracy related bias error (+/−SD)values. Alternatively, in one example, Block S150 can also present theestimated hemoglobin mass in the form of a final output value—such as“150 g”—and error-adjusted values—such as “135 g to 145 g” for a bias of“−10 g” with one standard deviation of “+/−₅ g”. In a foregoing example,Block S150 can similarly present the data to the user in the form of abias-compensated estimated amount of blood component—such as in the formof “140 g (130 g to 145 g).” Yet alternatively, Block S150 can displayan A_(RMS) value rather than or in addition to an SD value for the biaserror in the estimated amount of blood component. Block S162 canimplement similar methods or techniques to present aggregate bloodloss-related data to the user.

Blocks S150 and S162 can also graph the aggregate estimated amount ofblood component and aggregate estimated blood loss of the patient —andthe corresponding error values—over time as additional fluid receiversare imaged and analyzed. For example, Block S150 can display a graphicalrepresentation of the aggregate estimated hemoglobin mass as a functionof time with the bias error (and a standard deviation of the error)calculated in Block S140 following the aggregate estimated hemoglobinmass, such as in the form of: a white dashed line showing cEHM bias andtwo yellow dashed lines depicting standard deviation of the error oneach side of the dashed white line and offset from a solid green linedepicting aggregate estimated hemoglobin mass output in Block S140.Blocks S150 and S162 can similarly display a plot of error distribution(e.g., a histogram) relative to the corresponding aggregate estimatedhemoglobin mass and aggregate estimate blood loss of the patient,respectively.

Blocks S150 and S162 can also display estimated amounts of bloodcomponent and/or related error data in qualitative colors based on acorresponding patient risk level. The method can also trigger an alarmto prompt a user action based on a patient risk level estimated from theaggregate hemoglobin mass and/or aggregate estimated blood loss of thepatient, such as described in U.S. patent application Ser. No.14/072,625.

5. Variations

In a first variation, the method 100 can estimate an amount of a bloodcomponent and its associated bias error with respect to multiple volumesof fluid received by multiple fluid receivers captured in a singleimage. Preferably, the method implements machine vision in order toperform object detection (e.g., finding the pixel coordinates for abounding box encapsulating the target object) or object segmentation(e.g., finding the pixels that correspond to the target object) forautomatically detecting the regions corresponding to the fluidreceivers. Alternatively, the regions corresponding to the fluidreceivers can be manually detected by, for example, having the user(e.g., a nurse, an anesthesiologist, etc.) spread their fingers on atap-interface to draw a bounding box around each fluid receiver. Themethod can perform the object detection, object segmentation, or otherregion detecting approach using features extracted from a datarepresentation of the image (e.g., pixel values corresponding to colorintensities along the red, green, blue (RGB) scale). Preferably, thefeatures are explicitly selected and can include features such as colorhistograms, histogram of oriented gradients (HOG), scale-invariantfeature transform (SIFT), and/or bag-of-words. Alternatively, thefeatures are automatically selected through deep-learning approaches(e.g., recurrent neural networks, convolutional neural networks). Upondetecting the fluid receivers in the image, the method can perform BlockS130 to calculate the estimated amounts of the blood component and theirassociated bias errors in each of the fluid volumes received by each ofthe fluid receivers. Similarly, the method can perform Block S140 toupdate the analysis of the aggregate amount of the blood component andthe aggregate bias error.

In a first example of the first variation, the image contains multipletypes of fluid receivers (e.g., surgical textiles, canisters). In anillustration of the first example, an image contains three fluidreceivers: a surgical gauze sponge, a surgical towel, and a suctioncanister. The method estimates the amounts of blood component—and theassociated bias error—in the volumes of fluid for each of the surgicalgauze sponge, the surgical towel, and the suction canister. Using theestimates of the amounts of blood component and of the associated biaserrors, the method can update the analysis of the aggregate amount ofthe blood component and the aggregate bias error. Alternatively, Blocksof the method 100 can be performed on images that only contain multipleinstances of the same type of fluid receiver, such as an image onlycontaining two suction canisters.

In a second example of the first variation, the method 100 calculatesestimated amounts of different types of blood components (e.g.,extracorporeal hemoglobin, whole blood, platelet plasma, white bloodcells, etc.) for the volumes of fluid received by the fluid receivers.In an illustration of the second example, an image contains two fluidreceivers: a surgical gauze sponge and a suction canister. The computingsystem performs Block S130 to calculate estimated amounts of hemoglobinand platelet content for both the volume of fluid received by thesurgical gauze sponge and the volume of fluid received by the suctioncanister. The computing system can then perform Block S140 to update theanalysis of the aggregate amount of the hemoglobin, its associatedaggregate bias error, the aggregate amount of the platelet content, andits associated aggregate bias error.

As shown in FIGS. 7 and 8, in a second variation, the method furthercomprises determining a biological status of the patient derived fromthe aggregate amount of the blood component and the aggregate bias errorS170; and providing, at a user interface device in communication withthe computing system, a notification indicative of the biological statusS180. The method can determine and display a biological status of thepatient in terms of actionable recommendations (e.g., “Saline driprecommended”) and general statuses (e.g., “Normal blood loss” or “Lowplasma concentration”). Preferably, when the biological status comprisesan actionable recommendation, the recommendation takes a form thatrequires no further interpretation (e.g., “Allogeneic blood transfusionrecommended”). Alternatively, the actionable recommendations can take aform that requires further medical interpretation upon performance ofthe recommendation (e.g., “Warning: analyze estimated blood loss”). Thedetermination and display of the biological status is preferablyexecuted continuously in a substantially real-time manner.Alternatively, a patient's biological status can be updated in a moreperiodic manner, such as upon a manual request inputted by a user (e.g.,a nurse, an anesthesiologist, etc.). In Block S180, determination of thepatient's biological status preferably is generated from analysis of theaggregate amount of the blood component, the aggregate bias error, andthe associated derived information. Alternatively, the biological statuscan be generated from any of the information resources alone or incombination. Furthermore, in relation to U.S. patent application Ser.No. 14/072,625, Blocks S170 and S180 can be used to monitor a patient'seuvolemia status and/or guide responses to changes in the patient'seuvolemia status in any suitable manner.

In a first example of the second variation, the computing systemdetermines a patient's blood-related biological status based on ananalysis of the aggregate estimated blood loss and associated aggregatebias error. The computing system subsequently communicates the patient'sbiological status to a user interface that displays and/or notifies theuser of the patient's biological status as in Block S180. In the firstexample, different biological statuses are determined based on theaggregate volume of estimated blood loss that has occurred over a setperiod of time. A lookup table can be generated for template matchingpurposes, the lookup table containing biological statuses (e.g., “Normalamount of blood loss detected”) paired to defined ranges of estimatedblood loss over time (e.g., 1-1.5 mL/minute). When the aggregateestimated blood loss over time falls into a certain defined range thatis defined in the lookup table, the associated biological status isassigned to the patient and displayed to the user. Preferably, thetemplate matching process incorporates the aggregate bias error whenexamining the lookup table to determine the applicable defined range ofvalues and associated biological status. Alternatively, the settings ofthe template matching process can be, for example, manually set toassess estimated aggregate blood loss over time without considering theassociated aggregate bias error. However, when initially generating thelookup table, aggregate or individual bias errors can be computed forthe defined ranges of estimated amount of blood loss over time. In anillustration, a patient undergoing surgery loses 0.7 liters of bloodover a length of time that is shorter than expected for the averageindividual. The computing system examines the lookup table, anddetermines that the current estimated amount of aggregate blood lossover time falls into a range associated with the biological status of“Allogeneic blood transfusion recommended.” The computing system thentransmits the patient's biological status to the user interface, whichdisplays the status to the user as in Block S180.

In a second example of the second variation, the computing systemdetermines the patient's biological status based on the informationgenerated in Block S140, Block S150, as well as supplemental situationaldata. Preferably, supplemental and/or otherwise contextual datacomprises patient characteristics (e.g., medical history, genetics,gender, weight, age, height, race, health status, diet, etc.) andmedical procedure characteristics (e.g., type of surgery, location ofblood loss, localization of blood loss, etc.). In a first illustrationof the second example, the computing system determines the patient'sbiological status through a template matching model using a lookup tableas described above. Preferably, different lookup tables can be generatedand employed based on the various permutations of the supplementaland/or contextual data (e.g., a situation-specific lookup table can beemployed for a ₃₅-year old diabetic patient undergoing cardiac surgery).In a second illustration of the second example, a machine learning model(e.g., supervised, semi-supervised, unsupervised) is employed toclassify the patient into a particular biological status. In thisillustration, supplemental situational data as well as informationgenerated in Blocks S140 and S150 can be used as features upon which amachine learning classifier (e.g., support vector machines, softmax,linear, non-linear) can be built and trained.

As shown in FIGS. 4E and 5, in a third variation, a fluid receiveranalyzer status (“analyzer status”) notification (e.g., “Unknown fluidreceiver type may affect blood component analysis”) is provided at auser interface device upon detection that the aggregate or individualamount of blood component and/or the aggregate or individual bias erroris outside of a threshold range or amount. In this variation, calculatedoutput values outside a threshold range or amount may indicate that theBlocks of the method 100 were performed under non-ideal conditions, suchas when there is an unknown fluid receiver type, insufficient lighting,oversaturation, or poor resolution for captured images. Preferably, theanalyzer status notification indicates the conditions that led to themethod 100 outputting values outside the threshold range or amount.Alternatively, the analyzer status notification can generally alarm theuser that there are non-ideal conditions for blood component analysis.The analyzer status can also indicate a recommendation for the user toperform in order to remedy the conditions (e.g., “Use alternative methodsuch as gravimetric or visual estimation”). In examples, the analyzerstatus notification comes in the form of a visual alert (e.g., a pop upon the user interface, non-verbal symbol or image, alert with words orsentences). In other examples, the notification comes in the form of avibration (e.g., the user interface device vibrates). However, thenotification can come in any suitable form (e.g., auditory, touch-based,haptic, etc.). Preferably, the user is automatically notified when thecomputing system identifies that the outputted values are outside thethreshold range or amount. However, the user can also be notified basedoff of manually set user configurations and inputs (e.g., a user can settheir own threshold ranges or amounts)

In another variation, the method 100 can estimate an amount of a bloodcomponent and/or non-blood component and its associated bias error withrespect to volumes of fluid associated with fluid receivers that areintracorporeal (i.e., situated or occurring within the body).Preferably, the intracorporeal fluid receivers are biological componentssuch as organs, tissues, or cells. Alternatively, the intracorporealfluid receivers can be non-biological such as a medical device, animplant, or other non-biological component found within the body. Inthis variation, the method can estimate an amount of a blood componentand its associated bias errors from images of fluid receivers in theirintracorporeal state while still situated or found within the body.Alternatively, the method can estimate an amount of a blood componentand its associated bias errors from images of intracorporeal fluidreceivers that have been excised or removed from the patient's body. Inan illustration, the computing system calculates an estimated amount ofblood component and its associated error with respect to a patient'stissue that a medical professional excised during surgery.

As described above, the method 100 can calculate and present to a useraggregate error for a biometric value monitored during a surgery orother clinical effect, such as aggregate estimated patient blood lossestimated from a sum of blood volume estimates for individual fluidreceivers over time. However, in another variation, the method can alsocalculate error in non-aggregate biometric measurements. For example,the method can implement similar techniques to estimate and share with auser an error in a real-time hematocrit estimate of a patient or anerror in a patient blood oxygen saturation measured with a non-invasivepulse oximeter.

As shown in FIGS. 4A-4D, in another variation, the method trackshemoglobin mass, whole blood volume, and/or an amount of another bloodcomponent and the corresponding non-cumulating error values of anaccumulating parameter. For example, the method can estimate hemoglobinmass and aggregate blood volume contained in a surgical suction canisteras the canister fills with patient and irrigation fluids over time aswell as corresponding errors each time, such as for each instance thatthe method extrapolates a hemoglobin mass and/or a whole blood volumefrom an image of the canister (e.g., at a sample rate of 1 Hz). Thus,the method can implement techniques as described above to calculateblood-related quantities and related error values for a parameter thatis not aggregate (i.e., not a compilation of multiple discretequantities and associated errors) but is inherently aggregate in itscontents.

However, the method 100 can be applied to any other measured and/ormonitored biometric parameter to calculate a patient-related value, tocalculate an error associated with the patient-related value, and topresent the data to a user.

The systems and methods of the preferred embodiment can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with the application, applet, host, server,network, website, communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,or any suitable combination thereof. Other systems and methods of thepreferred embodiment can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are preferably executedby computer-executable components preferably integrated bycomputer-executable components preferably integrated with apparatusesand networks of the type described above. The computer-readable mediumcan be stored on any suitable computer readable media such as RAMs,ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives,floppy drives, or any suitable device. The computer-executable componentis preferably a processor but any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention as defined in the followingclaims.

We claim:
 1. A method for communicating estimated blood loss parameters of a patient to a user, comprising: at a computing system in communication with an image acquisition device, receiving data representative of an image, of a fluid receiver, wherein the image is generated by the image acquisition device; at the computing system, detecting a region within the image associated with a volume of fluid received at the fluid receiver, the volume of fluid including a blood component associated with blood loss of the patient; at the computing system, calculating an estimated amount of the blood component present in the volume of fluid based upon a parameter derived from the region, and determining an error associated with the estimated amount of the blood component; at the computing system, updating an analysis of an aggregate amount of the blood component and an aggregate error associated with blood loss of the patient, based upon the estimated amount of the blood component and the error; and at a user interface device in communication with the computing system, providing information derived from the analysis of the aggregate amount of the blood component and the aggregate error, to the user.
 2. The method of claim 1, wherein calculating the estimated amount of the blood component present in the volume of fluid comprises calculating an estimated mass of hemoglobin present in the volume of fluid, and wherein determining the error comprises determining a bias associated with the estimated mass of hemoglobin.
 3. The method of claim 2, wherein determining the bias comprises: generating a comparison between the estimated mass of hemoglobin and a set of ranges of hemoglobin masses, the set of ranges of hemoglobin masses determined from a set of fluid samples having a set of known hemoglobin masses paired with estimated hemoglobin masses derived from image analysis of each of the set of fluid samples within a version of the fluid receiver; selecting a range of the set of ranges based upon the comparison; and retrieving the bias and a standard deviation in the bias associated with the range.
 4. The method of claim 3, wherein updating the analysis of the aggregate amount of the blood component and the aggregate error comprises adding the bias and the standard deviation of the bias to a running total of the aggregate error and an aggregate standard deviation associated with the aggregate error.
 5. The method of claim 3, wherein determining the bias includes determining a demographic characteristic of the patient, and generating a comparison between the estimated mass of hemoglobin and a set of ranges of hemoglobin masses associated with the demographic characteristic of the patient in retrieving the bias.
 6. The method of claim 1, wherein calculating the estimated amount of the blood component present in the volume of fluid comprises calculating an estimated blood loss volume within the fluid receiver, and wherein determining the error comprises determining a bias associated with the estimated blood loss volume and a standard deviation associated with the bias.
 7. The method of claim 1, wherein the analysis of the aggregate amount of the blood component and the aggregate error is associated with a set of volumes of fluid received into a single fluid receiver.
 8. The method of claim 7, wherein the single fluid receiver comprises one of a canister, a surgical textile, and a fluid receiving container.
 9. The method of claim 1, wherein the analysis of the aggregate amount of the blood component and the aggregate error is associated with a set of volumes of fluid received at a set of fluid receivers, including the fluid receiver.
 10. The method of claim 9, wherein the set of fluid receivers includes at least two of: a canister, a test strip, an absorbent pad, a surgical textile, a sponge, a fluid receiving bag, a drape a cell salvage system, and a drain device.
 11. method of claim 1, wherein providing information derived from the analysis includes detection that the estimated amount of the blood component is outside of a threshold range.
 12. The method of claim 1, wherein providing information derived from the analysis includes providing the aggregate amount of the blood component and a range derived from the aggregate error, wherein the range does not include the aggregate amount of the blood component.
 13. The method of claim 1, wherein providing information derived from the analysis comprises providing a Bland-Altman Limits of Agreement generated from the aggregate amount of the blood component and the aggregate error.
 14. The method of claim 1, wherein updating the aggregate error comprises updating the aggregate error with the error and a standard deviation in the error.
 15. A method for communicating medical parameter information of a patient to a user, comprising: at a computing system, receiving data representative of a medical parameter; determining a statistical component associated with the medical parameter; at the computing system, updating an analysis of an aggregate amount of the medical parameter and an aggregate statistical component associated with the medical parameter, based upon the medical parameter and the statistical component; and at a user interface device in communication with the computing system, providing information derived from the analysis of the aggregate amount of the medical parameter and the aggregate statistical component, to the user.
 16. The method of claim 15, wherein updating the analysis of the aggregate statistical component comprises determining one or more of: a bias error, a confidence interval, a limit of agreement, a root mean square error, an interquartile range, a standard deviation, an error histogram, and a Monte Carlo simulation histogram associated with the medical parameter.
 17. The method of claim 15, wherein determining the statistical component comprises determining a statistical combination derived from one or more of: a bias error, a confidence interval, a limit of agreement, a root mean square error, an interquartile range, a standard deviation, an error histogram, and a Monte Carlo simulation histogram.
 18. The method of claim 15, wherein determining the statistical component associated with the medical parameter comprises identifying the statistical component from a table relating ranges of the medical parameter to values of the statistical component.
 19. The method of claim 15, wherein receiving data representative of the medical parameter comprises receiving data derived from one or more of: a hemoglobin-related parameter, a blood volume, a blood mass, a cell volume, a urine-related parameter, a saliva-related parameter, a lymphatic fluid-related parameter, a mucus-related parameter, a plasma-related parameter, a platelet-related parameter, an ammonic fluid-related parameter, an oxygen saturation-related parameter, a temperature-related parameter, a blood pressure-related parameter, a spinal fluid pressure-related parameter, and an inter-cranial pressure-related parameter.
 20. The method of claim 15, wherein receiving data representative of the medical parameter comprises receiving data representative of an image, at a computing system in communication with an image acquisition device, wherein the image is generated by the image acquisition device; and at the computing system, computing an amount of the medical parameter based upon analyzing a region within the image.
 21. The method of claim 20, wherein computing the amount of the medical parameter comprises extracting a color parameter from a region of the image.
 22. The method of claim 21, wherein the color parameter is transformed according to at least one of a parametric model and a template-matching model. 